Azure Stream Analytics

Built-in machine learning models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide.

Customers love Azure Stream Analytics for its ease of analyzing streams of data in movement, with the ability to set up a running pipeline within five minutes. Optimizing throughput has always been a challenge when trying to achieve high performance in a scenario that can't be fully parallelized.

Our goal in the Azure Stream Analytics team is to empower developers and make it incredibly easy to leverage the power of Azure to analyze big data in real-time. We achieve this by continuously listening for feedback from our customers and ship features that are delightful to use and serve as a tool for tackling complex analytics scenarios.

Do you know how to develop and source control your Microsoft Azure Stream Analytics (ASA) jobs? Do you know how to setup automated processes to build, test, and deploy these jobs to multiple environments?

Today, we are announcing the general availability of Azure Stream Analytics (ASA) on IoT Edge, empowering developers to deploy near-real-time analytical intelligence closer to IoT devices, unlocking the full value of device-generated data.